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  Structural decomposition of decadal climate prediction errors: A Bayesian approach

Zanchettin, D., Gaetan, C., Arisido, M. W., Modali, K., Toniazzo, T., Keenlyside, N., et al. (2017). Structural decomposition of decadal climate prediction errors: A Bayesian approach. Scientific Reports, 7: 12862. doi:10.1038/s41598-017-13144-2.

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 Creators:
Zanchettin, Davide, Author           
Gaetan, Carlo, Author
Arisido, Maeregu Woldeyes, Author
Modali, Kameswarrao1, Author           
Toniazzo, Thomas, Author
Keenlyside, Noel, Author
Rubino, Angelo, Author
Affiliations:
1Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, Bundesstraße 53, 20146 Hamburg, DE, ou_1479671              

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Free keywords: ATLANTIC MULTIDECADAL VARIABILITY; BIASES; MODEL; UNCERTAINTY; ENSEMBLES; PACIFIC; SSTScience & Technology - Other Topics;
 Abstract: Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.

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Language(s): eng - English
 Dates: 20172017-10-092017-10-09
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Project name : PREFACE MiKlip
Grant ID : 603521
Funding program : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)

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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: - Volume / Issue: 7 Sequence Number: 12862 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322